scientimate.welchpsd#

f, Sxx = scientimate.welchpsd(x, fs=2, SegmentSize=256, OverlapSize=0, WindowName='hamming', nfft=256, OutputSmoothSize=0, dispout='no')

Description#

Calculate power spectral density using Welch’s method

Inputs#

x: Input data
fs=2: Sampling frequency that data collected at in (Hz)
SegmentSize=256: Segment size, data are divided into the segments each has a total element equal to SegmentSize
OverlapSize=0: Number of data points that are overlaped with data in previous segments

OverlapSize is recomneded to be half of the SegmentSize
WindowName=’hamming’: Window name, define if multiplying input data by window function or not (‘none’: not multiplying)

‘none’,’rectangular’,’triangular’,’welch’,’hanning’,’hamming’,’gaussian’,’blackman’,’nuttall’,’blackmanharris’
nfft=length(x): Total number of points between 0 and fs that spectrum reports at is (nfft+1)
OutputSmoothSize=0: Window size for smoothing calculated spectrum (0, 1 or 2: not smoothing, reports original periodogram)

If WindowName=’none’ and OutputSmoothSize>2, then WindowName=’hamming’
dispout=’no’: Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs#

f: Frequency in (Hz)
Sxx: Power spectral density using Welch’s method (m^2/Hz)

Examples#

import scientimate as sm
import numpy as np
import scipy as sp
from scipy import signal

x=sp.signal.detrend(0.5*np.cos(2*np.pi*0.2*np.arange(0,1024,1/2))+(-0.1+(0.1-(-0.1)))*np.random.rand(1024*2))
f,Sxx=sm.welchpsd(x,2,256,128,'hamming',2048,0,'yes')

References#

Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), 70-73.

scientimate.welchpsd

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